Influence maximization of informed agents in social networks

Volume: 254, Pages: 229 - 239
Published: Mar 1, 2015
Abstract
Control of collective behavior is one of the most desirable goals in many applications related to social networks analysis and mining. In this work we propose a simple yet effective algorithm to control opinion formation in complex networks. We aim at finding the best spreaders whose connection to a reasonable number of informed agents results in the best performance. We consider an extended version of the bounded confidence model in which the...
Paper Details
Title
Influence maximization of informed agents in social networks
Published Date
Mar 1, 2015
Volume
254
Pages
229 - 239
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